Practical Routing for Delay Tolerant Networks

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					Practical Routing for Delay
        Tolerant Networks

                   Evan Jones
                      Lily Li
                   Paul Ward
The Problem: Routing in DTNs

Get data from the source to the destination without
 an end-to-end connection
Previous Work: Epidemic Routing
  Eventually, all buffers contain the same messages

  Very robust
  Zero knowledge

  Many messages exchanged
  Need large buffer
Previous Work: Shortest Paths

  Minimize metric to minimize resources consumed

 Few transmissions
 Low buffer requirements

  Requires predictable schedules
Design Goals
   Self configuring
   Robust to changes and failures

 Efficient use of buffer and network resources

 Reliable delivery
Optimization Criteria
  Maximize delivery ratio
  Minimize delay
  Minimize buffer consumption
  Minimize number of transmissions
Path Metrics: Expected Delay
  Minimum Expected Delay (MED)
    Compute the expected delay for each hop
    Minimize end-to-end expected delay

  Minimum Estimated Expected Delay (MEED)
    Compute expected delay for the observed history
Topology Distribution: Link State
Natural match for epidemic protocol

    Link state: flood link state to all nodes
    Epidemic: propagate a message to all nodes

    Complete update after a single exchange
Routing Decision Time
  Source routing
    Cannot react to topology changes
  Per hop routing
    If messages wait for a long time, same problem
  Per contact routing
    Recompute routing for all messages on each connection
    Takes advantage of opportunistic connectivity
    Frequently recompute routing table
Short Circuiting
When link is up: link cost = link latency

    Permits messages to take advantage of good timing
Short Circuiting
Short Circuiting
Loop Free Routing

  Must make decisions with the same state

Traditional networks
  State does not change while data is in transit

Delay tolerant networks
 Want to be able to adapt while data is in transit
Performance Evaluation

 Compare five protocols:
   Earliest Delivery (ED)
   Minimum Expected Delay (MED)
   MED Per Contact
   Minimum Estimated Expected Delay (MEED)
 Network layer simulator

 Based on wireless LAN usage traces from
 Dartmouth College
   More than 2000 users
   More than 500 access points
   2 years
 Represents mobile users forming an ad-hoc DTN
 “Random” mobility with statistical regularity
Dartmouth Data
Dartmouth Data
Scenario Generation

Too much data!
   Only use one month of data
   Select 30 connected users

1.   Pick a node at random
2.   Put its “good” neighbours in a set
3.   Select node at random from the set
4.   Repeat 2 until you have N nodes
Simulation Parameters

 30 nodes
 10 topologies
 Bidirectional traffic
 Each node sends 12 messages every 12 hours
 10 000 bytes per message
Delivery Ratio Over Buffer Size
Latency Over Buffer

 Link state is an excellent fit with epidemic
 MEED: Reasonable performance without
 Epidemic performance is buffer limited
   Close to optimal with lots of resources
 Per-contact routing
   Decreases delay
Future Work
 Different data sets
 Multiple copies

 Experimental deployments of DTNs
 Better metrics
 Use topology for directed multiple copy routing

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